How to Use the USGS Earthquakes MCP in LlamaIndex

Q: How can I use USGS Earthquakes and LlamaIndex together to analyze trends?

First, use queryearthquakes to gather a batch of data. Second, index that output using LlamaIndex's capabilities. Then you can query the index months later for comparisons or pattern recognition.

Index and Query Earthquake History with LlamaIndex's Knowledge Graphing.

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Connect USGS Earthquakes MCP to LlamaIndex

Create your Vinkius account to connect USGS Earthquakes to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup USGS Earthquakes with LlamaIndex

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Indexing USGS Earthquakes Data for RAG

LlamaIndex turns raw API outputs into a searchable knowledge base, meaning you don't just see the results—you build an index out of them. Use `get_significant_30_days` to pull recent quake data and immediately load that output into your vector store. The next time you ask a question about 'major quakes last month,' LlamaIndex grounds the answer in the indexed, factual seismic data, eliminating hallucinations.

Targeted Historical Searches with MCP Server

Need to compare conditions? Query past events using `query_earthquakes` by setting precise start and end times. Instead of just printing a list, LlamaIndex indexes the parameters (like minimum magnitude or specific lat/long boundaries) alongside the results. This allows you to query historical configurations: 'What were the characteristics of all quakes over 5.0 magnitude that happened in this region last year?'

Using Count Metrics for Knowledge Augmentation

The `count_earthquakes` tool gives you a metric, but LlamaIndex makes that metric searchable knowledge. You can index the *concept* of 'high activity count' alongside specific regions and timeframes. This is powerful when combining multiple data sources; you query the document store for policy guidelines, then augment those answers with indexed counts from the MCP Server.

Setup guide

Set up USGS Earthquakes MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all USGS Earthquakes MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to USGS Earthquakes tools.",
)
response = await agent.run("List recent USGS Earthquakes data")

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Common questions about USGS Earthquakes MCP in LlamaIndex

First, use `query_earthquakes` to gather a batch of data. Second, index that output using LlamaIndex's capabilities. Then you can query the index months later for comparisons or pattern recognition.

The server provides real-time seismic and earthquake data, including event coordinates, magnitudes, and time stamps. LlamaIndex makes these structured API outputs retrievable via semantic search.

Yes. By indexing the raw data output from the server, LlamaIndex ensures that all answers are grounded in the actual API responses, not generalized knowledge or guesses.

You can set up your RAG pipeline to periodically re-index the results from `get_significant_30_days`. This keeps your knowledge base current without needing manual intervention.

The server touches seismic and earthquake data. Specifically, it handles structured records containing time, location (lat/long), and magnitude.

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